Description
Applied Stochastic Modelling (2nd Ed.)
Chapman & Hall/CRC Texts in Statistical Science Series
Author: Morgan Byron J.T.
Language: EnglishSubjects for Applied Stochastic Modelling:
Keywords
Negative Log Likelihood; maximum; Armillaria Root Rot; likelihood; Matlab Program; estimate; Data Sets; matlab; Posterior Model Probabilities; program; Log Likelihood Surface; random; Posterior Distribution; variable; SQP Algorithm; poisson; Metropolis Hastings Method; distribution; Quantal Response Data; beta; Maximum Likelihood Estimator; Maximum Likelihood; Em Algorithm; Gibbs Sampling; Grey Heron Ardea Cinerea; 10th 11th 12th 13th 14th; Replicate Data Sets; Canonical Link Function; Monte Carlo Maximum Likelihood Estimate; Helichrysum Bracteatum; Likelihood Ratio Test; Cumulative Distribution Function; Prior Distribution; Kernel Density Estimate; Forest Transects
Publication date: 10-2017
· 17.8x25.4 cm · Hardback
Publication date: 11-2008
320 p. · 15.6x23.4 cm · Paperback
Description
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Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and improved figures, this edition offers numerous updates throughout.
New to the Second Edition
- An extended discussion on Bayesian methods
- A large number of new exercises
- A new appendix on computational methods
The book covers both contemporary and classical aspects of statistics, including survival analysis, Kernel density estimation, Markov chain Monte Carlo, hypothesis testing, regression, bootstrap, and generalised linear models. Although the book can be used without reference to computational programs, the author provides the option of using powerful computational tools for stochastic modelling. All of the data sets and MATLAB® and R programs found in the text as well as lecture slides and other ancillary material are available for download at www.crcpress.com
Continuing in the bestselling tradition of its predecessor, this textbook remains an excellent resource for teaching students how to fit stochastic models to data.